The operational discipline of monitoring, testing, and governing AI agents once they are running in production, not just when they are demoed.
Also known as Agent Operations, Agent Reliability Engineering
AgentOps is the operational discipline built around AI agents that act on their own, calling tools, making decisions, and completing multi-step tasks without a human approving every move. It covers the same ground DevOps covers for software and MLOps covers for machine learning models: deployment, monitoring, logging, testing, and incident response, but tuned to agents whose behavior can shift from one run to the next.
Where a traditional software bug produces the same failure every time, an agent can take a different path through the same task on two separate runs. AgentOps tooling tracks every step an agent takes, which tools it called, what it decided, what it changed, flags runs that drift from expected behavior, and gives a team a way to roll back or intervene before a bad decision reaches a customer or a client account.
Agencies are moving fast from running an agent in a demo to letting an agent touch live client accounts and budgets every day, and that shift changes what accountability looks like.
Client trust depends on visibility. When an agent adjusts a media budget, drafts copy, or updates a CRM record, the agency needs a record of what it did and why, the same way a human account manager would document a decision.
Failures compound quietly. An agent that misreads a brief once might repeat the same mistake across every asset it touches that day. AgentOps practices such as logging, alerting, and staged rollouts catch that early instead of after a client notices.
It is becoming a vendor question. As more martech platforms bolt agents onto existing tools, agencies evaluating them should ask how the vendor monitors and governs agent behavior, not just what the agent can do on a good day.
An agency running an AI agent that pulls creative performance data, flags underperforming ads, and drafts pause or scale recommendations sets up basic AgentOps before letting it touch a live account. Every recommendation gets logged with the data behind it, a human strategist reviews the agent’s reasoning before budget changes go live, and the team reviews a weekly log of agent decisions to catch patterns, like the agent consistently misreading a client’s seasonal spend calendar, before they become a recurring client complaint.
The automations and agents module of the workshop teaches you how to build AI workflows that compress the busywork without taking the craft out of the studio.